Vice President - Data Science/Applied AI ML

Testing Site - JPMC; Bengaluru; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

Testing Site - JPMC in Bengaluru leads the CCOR Financial Crime Data Science team to design and deploy production-grade ML solutions for AML monitoring with regulatory alignment.

The role focuses on research, feature engineering, model risk management, and scalable MLOps pipelines to reduce false positives while maintaining compliance. You will mentor a high-performing team and drive responsible AI practices in a fast-paced banking environment.

Qualifications

• Master's or PhD in a quantitative discipline.
• 10+ years hands-on ML experience, with at least 5+ years in Financial Crime Compliance (AML/sanctions/fraud).
• Proven leadership delivering production ML for financial crime at enterprise scale.
• Advanced Python skills; strong experience with ML frameworks.
• Expertise in supervised learning, anomaly detection, semi-supervised learning, clustering, feature stores, and calibration/threshold optimization.
• Demonstrated experience in model risk management: documentation, validation, benchmarking/challenger models, backtesting, stability and drift analysis, explainability for regulatory review.
• Excellent communication to translate complex models with clear reason codes and influence stakeholders.
• People leadership: recruiting, coaching, performance management, inclusive culture.

Responsibilities

• Lead the CCOR Financial Crime Data Science team to design, deploy, and operate production-grade ML solutions for AML monitoring with regulatory alignment.
• Drive research and applied innovation in learning methods and graph analytics to improve true positives and reduce false positives.
• Own end-to-end model lifecycle: problem framing, data sourcing, features, model development, validation, thresholding, and retraining.
• Maintain robust model risk management practices across the lifecycle in partnership with Risk and Audit.
• Build and maintain MLOps pipelines, model registries, automated monitoring, and governance artifacts for scalable production operations.
• Collaborate with FCC, Investigations, Operations, and Technology to translate red flags into defensible ML controls.
• Enhance investigator decisioning via interpretable ML, SHAP/LIME, and human-in-the-loop feedback loops.
• Mentor, hire, and develop a high-performing team; promote scientific rigor and ethical AI.
• Maintain pragmatic use of GenAI/LLMs as adjuncts to classical ML methods.

Skills

Advanced Python

ML frameworks

Leadership

Communication

MLOps

Regulatory knowledge

Graph analytics

Anomaly detection

Explainability

Feature stores

Imbalanced learning

Model risk management

Education

Master's or PhD in Computer Science/Statistics/Math/Economics/OR

Tools

SHAP

LIME

PyTorch

TensorFlow

MLflow

CI/CD tools

Model registries

Job description

Job Responsibilities:

• Lead the CCOR Financial Crime Data Science team to design, deploy, and operate production-grade ML solutions across AML transaction monitoring use cases, with a strong focus on measurable risk mitigation and regulatory alignment.
• Drive research and applied innovation in supervised/unsupervised/semi-supervised learning, graph/network analytics, anomaly detection, and weak supervision to improve true-positive rates, reduce false positives, and enhance investigator productivity.
• Own end-to-end model lifecycle: problem framing, data sourcing/controls, feature engineering (customer/behavioral/temporal/graph features), model development, validation, calibration/thresholding, bias/fairness checks, monitoring, and retraining.
• Maintain rigorous model risk management practices across Model lifecycle, partnering with Model Risk and Internal Audit.
• Build and maintain robust MLOps pipelines (CI/CD for ML), model registries, automated monitoring (data drift, concept drift, performance), and governance artifacts to ensure reliable, scalable production operations.
• Partner with Financial Crime Compliance (FCC), Investigations, Operations, and Technology to translate typologies, red flags, and regulatory expectations into defensible ML controls and measurable control effectiveness.
• Enhance investigator decisioning through interpretable ML: deploy explainability techniques (e.g., SHAP, LIME, counterfactuals), stable reason codes, and human-in-the-loop feedback loops to continuously improve model precision and usability.
• Mentor, hire, and develop a high-performing team of data scientists/ML engineers/analysts; promote a culture of scientific rigor, ethical AI, and continuous learning.
• Maintain a pragmatic view of GenAI/LLMs as complementary tools (e.g., narrative generation for cases, unstructured doc parsing) while prioritizing classical/statistical/graph ML methods for core detection efficacy.

Required Qualifications and Skills:

• Master's or PhD in a quantitative discipline (Computer Science, Statistics, Mathematics, Economics, Operations Research, or related).
• 10+ years of hands-on ML experience, with at least 5+ years in Financial Crime Compliance, AML, sanctions, fraud, or related risk domains; deep knowledge of regulatory expectations (e.g., AML program requirements, sanctions controls, model governance).
• Proven leadership delivering production ML for financial crime, including transaction monitoring models, risk scoring, anomaly detection, network/graph analytics, and/or investigator triage/prioritization at enterprise scale.
• Advanced Python skills; strong experience with ML frameworks.
• Expertise in supervised learning, anomaly detection, semi-supervised learning, clustering, feature stores, and calibration/threshold optimization; familiarity with imbalanced learning and cost-sensitive evaluation.
• Demonstrated experience in model risk management: documentation, validation, benchmarking/challenger models, backtesting, stability and drift analysis, champion/challenger governance, and explainability suitable for regulatory review.
• Excellent communication skills to translate and explain complex models with clear reason codes, and influence cross-functional stakeholders and senior leadership.
• People leadership: recruiting, coaching, performance management, and fostering an inclusive, high-accountability culture.